Metal Oxides in Surface Sediment Control Nickel Bioavailability to

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Metal oxides in surface sediment control nickel bioavailability to benthic macroinvertebrates Raissa Marques Mendonca, Jennifer M Daley, Michelle L Hudson, Christian Schlekat, G. Allen Burton, and David Costello Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b03718 • Publication Date (Web): 18 Oct 2017 Downloaded from http://pubs.acs.org on October 18, 2017

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Environmental Science & Technology

TOC (Table of Contents Art)

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ACS Paragon Plus Environment


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Metal oxides in surface sediment control nickel bioavailability to benthic

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macroinvertebrates

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Raissa Marques Mendonca1*, Jennifer Marie Daley2, Michelle Lynn Hudson2, Christian Eduard

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Schlekat3, Glenn Allen Burton, Jr.2, David Matthew Costello1

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Department of Biological Sciences, Kent State University, 1275 University Esplanade, Kent,

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Ohio 44242, United States

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2

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Arbor, Michigan 48109, United States

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Parkway, Suite 240, Durham, North Carolina 27713, United States

School for Environment and Sustainability, University of Michigan, 440 Church St., Ann

Nickel Producers Environmental Research Association (NiPERA, Inc.), 2525 Meridian

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Corresponding author: Raissa M Mendonca Department of Biological Sciences, Kent State University 1275 University Esplanade, Kent, OH 44242 USA Tel: (330) 554-6244 Email address: [email protected]


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Abstract

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In aquatic ecosystems, the cycling and toxicity of nickel (Ni) are coupled to other elemental

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cycles that can limit its bioavailability. Current sediment risk assessment approaches consider

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acid-volatile sulfide (AVS) as the major binding phase for Ni, but have not yet incorporated

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ligands that are present in oxic sediments. Our study aimed to assess how metal oxides play a

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role in Ni bioavailability in surficial sediments exposed to effluent from two mine sites. We

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coupled spatially explicit sediment geochemistry (i.e., separate oxic and suboxic) to the

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indigenous macroinvertebrate community structure. Effluent-exposed sites contained high

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concentrations of sediment Ni and AVS, though roughly 80% less AVS was observed in surface

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sediments. Iron (Fe) oxide mineral concentrations were elevated in surface sediments and bound

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a substantial proportion of Ni. Redundancy analysis of the invertebrate community showed

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surface sediment geochemistry significantly explained shifts in community abundances. Relative

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abundance of the dominant mayfly (Ephemeridae) was reduced in sites with greater bioavailable

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Ni, but accounting for Fe oxide-bound Ni greatly decreased variation in effect thresholds

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between the two mine sites. Our results provide field-based evidence that solid-phase ligands in

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oxic sediment, most notably Fe oxides, may have a critical role in controlling nickel

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bioavailability.

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Introduction

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Sediment metals can impair biodiversity and ecological functions in aquatic

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environments.1-4 However, the cycling and toxicity of metals, such as nickel (Ni), are coupled to

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other elemental cycles that determine toxic metal speciation and sorption behavior onto aqueous

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and solid phase ligands. In sediment, metal mobilization is determined by binding (i.e., sorption,



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precipitation, chelation) to reduced and oxidized ligands, which controls metal bioavailability

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and toxicity.5,6 The distribution of solid-phase ligands in natural sediments is complex and often

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characterized by spatial stratification and temporal variation related to dynamic physicochemical

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conditions. In oxic sediment, oxidized species (i.e., iron, manganese, and aluminum oxide

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minerals) represent the major binding phases, whereas sulfide minerals are the predominant

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metal ligand in anoxic sediment layers.5 Lotic sediments are often vertically stratified with thin

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(mm to cm) aerobic layers overlying anaerobic horizons, and the distinct physicochemical

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conditions in these sediment layers modify their metal binding capacity. Unlike for sulfide,7

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binding constants for oxide minerals and the partitioning behavior of nickel to these solid phases

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in situ have not yet been applied to sediment metal risk assessment efforts.5

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Bioavailability models based on acid-volatile sulfide (AVS) and simultaneously extracted

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metals (SEM) have been extensively applied in metal toxicity guidelines and assume sulfide as

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the main factor controlling metal partitioning.8,9 The SEM-AVS models provide important

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information for predicting conditions when sediment metals are present but non-toxic for several

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aquatic species, but in some cases these models fail to predict metal toxicity due to unique

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sediment redox chemistry in surface sediment and benthic fauna ecology.10-13 In oxic surface

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sediment, AVS concentrations are generally low and highly variable owing to the rapid oxidation

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of sulfide in the presence of oxygen.12,14-16 In addition, oxic microenvironments can occur at

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deeper sediment depths that are oxygenated through diffusion via bioirrigation and bioturbation

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by benthic organisms.17,18 These variable sediment redox gradients can render SEM-AVS models

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less suitable for predicting metal bioavailability in naturally stratified sediment, and for deriving

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toxicity thresholds for taxa with unique bioturbation behavior.19 This limitation generally results


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in conservative criteria that do not account for metal binding and co-precipitating with oxide

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minerals.12,20

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Incorporating concentrations of oxidized ligands into metal toxicity assessments may

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provide benchmarks more compatible with environmental conditions, especially for lotic

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ecosystems.12,14,21,22 In addition to including metal oxide minerals (i.e., Fe, Mn, and Al oxides) as

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potential binding phases, accounting for the distinct geochemistry in surface sediments should

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also provide more realistic estimates of geochemical conditions under which sensitive taxa are

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exposed to sediment metals. While the SEM-AVS models can be effective at distinguishing

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between non-toxic and potentially toxic conditions in some sediments, the model’s limited

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applicability to only anoxic conditions constrains its scope in metal bioavailability assessment.

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To address this limitation, research on the behavior of metal partitioning onto metal oxide

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minerals in natural sediments is needed to derive information on binding coefficients. Analyses

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of toxic metals binding to metal oxide minerals have been widely performed on pure synthetic

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mineral substrates,23-25 but the studies did not examine metal binding within the context of the

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mixture of competing ligands found in natural sediments. With the potential importance of oxic

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sediment layers in modifying metal bioavailability, there is a need to explicitly test whether

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including oxidized species in models of bioavailability more accurately predicts sediment metal

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toxicity.

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In this study, we considered whether spatially resolved sediment geochemistry (i.e.,

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separate oxic and suboxic sediment) and water physicochemistry increased our ability to predict

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the effects of nickel exposure to macroinvertebrates compared to current methods (i.e.,

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homogenized sediment and SEM-AVS bioavailability models). Furthermore, we used in situ

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natural gradient of total Ni from two mine sites to determine if concentration–response



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thresholds could be determined from the field using indigenous benthic macroinvertebrates. We

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hypothesized that sediment geochemistry would be vertically stratified between oxic and anoxic

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horizons and characterized by a greater proportion of metal oxide minerals and low AVS in the

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surface layer due to oxidizing conditions. Additionally, we predicted sediment characteristics in

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surface sediments (e.g., total nickel concentration, nickel bound to solid-phase ligands) would

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significantly affect benthic macroinvertebrate composition and explain community variation

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between sites. Within effluent-exposed tributaries, we predicted the natural nickel gradient

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would reveal spatial patterns of invertebrate abundance, with sites farther from the effluent

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discharge more similar to reference conditions. Finally, we hypothesized that nickel-

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contaminated lotic sediments would be less toxic to the native invertebrate community than

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expected by current benchmarks due to the partitioning of nickel to metal oxide minerals in the

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oxic sediment layer.

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Methods

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Site description

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Sampling was conducted in tributaries of the Burntwood River near Thompson,

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Manitoba, Canada in August 2014. Two pairs of reference and effluent-exposed tributaries were

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selected near the Birchtree (55° 41’ N, 97° 57’ W) and Thompson (55° 49’ N, 97° 41’ W) nickel

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mines (Fig. S1). The tributary in the Birchtree mining area received effluent from the mine

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treatment plant, whereas effluent from the Thompson mine passed through a wetland complex

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(approximately 9 km) prior to reaching the sampling area (Fig. S1). Paired reference tributaries

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were within 8 km of effluent-exposed tributaries and had similar sediment characteristics but Ni

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at background concentrations. Within tributaries, ten sampling sites were spatially distributed


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from upstream to downstream, which established a gradient of nickel contamination in effluent-

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exposed sites (Fig. S2-S4).

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Water, sediment and invertebrate sampling and analysis

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Surface water physicochemical characteristics were assessed in situ with hand-held

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meters and a logging sonde (YSI 6290). Unfiltered surface water samples were collected for

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analysis of water hardness (as mg CaCO3 L-1) by EDTA titration. Additional surface water

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samples were filtered (0.45 µm) and acidified with 1% nitric acid for analysis of Ni and major

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anions (nitrate, sulfate, and chloride) via inductively-coupled plasma mass spectrometry (ICP-

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MS) and ion chromatography, respectively. Intact sediment cores were collected using 5-cm

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diameter plastic tubes and caps. The tubes were manually pressed into the sediment, plastic caps

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were placed at the ends of the core tube, and the intact core was carefully removed. Sediment

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cores with visible disturbance of surface sediment were discarded and new cores were collected

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within a 50-cm radius of the original core. At each reference tributary, sediment cores were

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collected at four sites (Ref 1–4), whereas sediment samples were collected at 10 sites within both

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exposure tributaries (n total = 28 sediment cores). The intact sediment cores were processed

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within 6 h by removing the overlying water, extruding the sediment using a plunger,

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and sectioning the core into surface sediment samples (0–2 cm) and deep sediment samples (2–4

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cm). Surface and deep sediment samples were sealed in polyethylene bags and frozen (-18° C)

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prior to analysis.

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Prior to geochemical analysis, sediment samples were defrosted and manually

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homogenized without opening the bags. Analysis of AVS and SEM was performed according to

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Allen and colleagues,26 which used 1M hydrochloric acid extraction (50 min) to obtain dilute



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acid-extractable metal concentrations (i.e., SEM). Total metal concentrations (FeTOT, MnTOT, and

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NiTOT) were obtained through sediment digestion in concentrated nitric acid at reflux

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temperatures using a digestion block. For oxidized Fe and Mn and associated metals, selective

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extractions27 were used to differentiate between the amorphous (MeHFO), crystalline (MeCFO), and

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total oxidized minerals (MeOXID). Metal concentrations (Fe, Mn, and Ni) for SEM metals, total

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metals, and selective extractions were determined by inductively coupled plasma optical

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emission spectrometry (ICP-OES). An elemental analyzer was used to determine carbon content

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of sediment samples. All analyses were completed with appropriate quality assurance/quality

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control checks (Table S1), which included blanks, duplicates, and a standard reference material

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(total metals only, Domestic Sludge, NIST SRM 2781).

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Benthic macroinvertebrate community samples were taken using a 152 x 152 mm petite

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ponar grab (surface area 231 cm2, depth 5–7 cm). Samples were collected at each of the ten

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sites within the reference and exposure tributaries (total n = 40 invertebrate samples). The

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sediment collected with the ponar was sieved through a 500-µm bucket sieve, washed, and

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manually transferred into polystyrene specimen vials filled with 90% ethanol. All invertebrates

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within each sample were sorted and identified to family28 using a dissecting microscope. All

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pelagic taxa identified were excluded from statistical analyses.

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Statistical analysis

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Sediment geochemical characteristics were analyzed with three-way analysis of variance

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(ANOVA) that used location (Birtchtree or Thompson), tributary (Ref or Exp), and sediment

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depth (surface or deep) as predictor variables. When necessary, data were log transformed to

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meet assumptions of ANOVA. Analyses for NiTOT required an additional statistical block for site


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to account for the variation related to the effluent dilution gradient. Concentrations of select

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extractions of metal (i.e., SEM, oxidized metal extractions) were expressed as a percentage of the

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total concentration of metal. When significant interactions were identified, post-hoc Tukey’s

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multiple comparison tests were used to detect significant pairwise differences. Water

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physicochemical properties were analyzed by analysis of covariance (ANCOVA) with location

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(Birchtree or Thompson) as a main effect and sediment total nickel (mean of surface and deep)

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as a covariate to account for the gradient in effluent exposure.

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Invertebrate community composition was analyzed via transformation-based redundancy

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analysis (RDA) to assess the proportion of variance in the invertebrate community explained by

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the environmental variables.29 The objective is to obtain components of the invertebrate

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community matrix that can be linearly explained by the predictor matrix (i.e., environmental

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variables) and that represent most of the variance in the community. In reference tributaries,

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invertebrate community samples that lacked paired sediment geochemistry measures were

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assigned mean values from sampled reference sites in the respective tributaries (n = 4).

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Invertebrate taxa abundances were transformed using Hellinger distance, which avoids the use of

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dual absences as an indication of similarity between sites. All benthic taxa identified (n = 38

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families) were included in the statistical analysis (Table S2). Four RDA models comprising

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different sets of explanatory variables were considered: water only, surface sediment only, deep

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sediment only, and all environmental variables. The statistical significance of each RDA model

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was tested by permutation with a type I error rate (α) of 0.05. Forward selection on variables

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within significant models was used to determine the most parsimonious model based on adjusted

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R2 and variable significance (α = 0.05) as selection criteria.30 Final models are depicted in



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ordination plots, and taxa with a significant proportion of variation explained by any RDA axis

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(coefficient of determination > 0.20), referred to as best-fit families, are identified.

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The most abundant and responsive mayfly taxon (i.e., Ephemeridae) was further analyzed

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using log-linear regression to assess responses to Ni exposure. Ephemeridae relative abundance

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(% of total invertebrate abundance) was regressed against three potential measures of Ni

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bioavailability: simultaneously extracted nickel (NiSEM), SEM Ni in excess of AVS corrected for

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organic carbon ((NiSEM-AVS)/ƒOC), and SEM Ni in excess of oxide-bound Ni corrected for

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organic carbon ((NiSEM-NiHFO)/ƒOC). The latter fraction represents the concentration of NiSEM that

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is not sorbed to amorphous metal oxides, which is similar to how NiSEM-AVS represents the Ni

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that is not precipitated as NiS. These measures of sediment Ni were selected for comparison due

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to their use in current sediment risk assessment (i.e., (SEM-AVS)/ƒOC)7 or their identification as

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important variables in this study (i.e., FeHFO explained significant variation in the community

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matrix). All measures of Ni and binding ligands were from measurement in surface sediments. A

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sigmoid log-logistic model, which is the conventional model for laboratory concentration–

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response, could not be fit for each mine location due to greater inherent variability and a limited

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sample size. Thus, for each mine location, a simpler log-linear regression was used to derive

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concentration–response relationships. Log-linear regression requires positive concentrations,

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therefore sites with negative values for Ni bioavailability (e.g., (SEM-AVS)/fOC < 0) were

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substituted with the minimum positive value measured in the corresponding mine location and

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tributary. We chose this approach because we wanted to use all the invertebrate data available,

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and if negative values were excluded from the regression, the dataset would be biased towards

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only those sites with greater concentrations of bioavailable Ni. From each concentration–

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response curve, we determined the concentration that reduced the abundance of Ephemeridae by


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20% (EC20) relative to the abundance of Ephemeridae in downstream effluent-exposed sites

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with minimal measurable exposure to the effluent (i.e., NiTOT ≤ reference mean NiTOT + 1

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standard deviation, n = 2 sites). The downstream Ephemeridae relative abundance was used as a

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baseline in lieu of the mean abundance at reference sites because in general the reference

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tributaries had greater abundance of taxa not found at effluent-exposed sites (e.g., Gammaridae,

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Polycentropodidae; Table S2), which lead to lower mean relative abundance of Ephemeridae in

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reference sites. For each bioavailability measure we calculated a relative difference in EC20

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between each mine site (RD = |EC20BIRCHTREE-EC20THOMPSON|/mean EC20) to assess how well

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the bioavailability model normalized the concentration–response relationship (i.e., lower RD

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indicates better agreement between locations). For two mine sites AVS was in excess of NiSEM;

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for comparative purposes, we derived thresholds for concentration-response relationships that

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both included corrected concentrations (i.e., set negative (SEM-AVS)/fOC to lowest positive

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value) and excluded these negative values. Concentration-response relationships were attempted

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for additional sensitive taxa (e.g., Talitridae, Caenidae) but the absence of organisms in over

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25% of the sampled sites prevented the detection of nickel-related effects.

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Reported values are either means ± standard deviation or for log-normally distributed

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variables geometric means ± geometric standard deviation. All statistical analyses were

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performed using RStudio31 with appropriate packages for generalized linear regressions (lme4

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version 1.1-11)32 and community ordinations (vegan version 2.3-2).33

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Results

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Sediment geochemistry

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Acid volatile sulfide concentrations did not differ between Thompson and Birchtree

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locations (p = 0.30) but varied significantly with sediment depth (p < 0.001) and between

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reference and effluent-exposed tributaries (p = 0.017; Fig. 1A). All surface sediment (geometric

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mean = 0.08 µmol g-1 dw, geometric SD = 5.4) had on average 82% less AVS than deep

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sediment (geometric mean = 0.48 µmol g-1 dw, geometric SD = 4.9). Effluent-exposed sites had

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greater AVS concentrations (maximum = 7.6 µmol g-1 dw) than reference tributaries. In effluent-

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exposed tributaries, elevated sulfate concentrations in mine effluent provided abundant substrate

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for microbial reduction of sulfur and precipitation of metal sulfide minerals. Sediment carbon

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content was consistent across mine locations and between surface and deep horizons (1.5–9.2%),

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but differed marginally between reference and effluent-exposed tributaries in Birchtree sediment

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(2% difference, p = 0.03).

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The concentration of total Fe (FeTOT) ranged from 2.1–3.9% in all sediment samples and

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was consistent among sediment depths and mine locations, only differing slightly between

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Thompson reference and effluent-exposed tributaries. The proportion of dilute acid-extracted

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iron (FeSEM) was significantly greater in Birchtree sediments and reference tributaries (p
0.20), and aligned near reference sites, which suggests

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that these taxa were most abundant in sites with lower total NiSURF concentrations and/or greater

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concentrations of FeHFO. The RDA identified a single tolerant invertebrate family (i.e.,

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Sphaeriidae (SPH)), whose abundance was significantly explained by the RDA but aligned

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closer to effluent-exposed sites with higher total NiSURF. Both the simplified models for

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RDAWATER and RDASURF explained a significant proportion of the variation in the

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macroinvertebrate community (p < 0.001).

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Ephemeridae concentration–response

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The relative abundance of Ephemeridae (Ephemeridae abundance/total invertebrate

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abundance at each site) declined with increasing concentrations of NiSEM in the surface sediment,

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but the concentration–response relationships differed among mine locations (Figure 3A).

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Ephemeridae relative abundance in Birchtree declined at a slightly lower NiSEM concentration

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(0.6 µmol g-1) than those in Thompson (0.8 µmol g-1, EC20 relative difference (RD) = 0.28). Ni

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concentrations in excess of AVS corrected for organic carbon also demonstrated concentration–

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response relationships with Ephemeridae relative abundance, with increasing concentrations of

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(NiSEM-AVS)/ƒOC reducing the abundance of mayflies (Figure 3B). However, a large difference

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in concentration–response thresholds was observed between Birchtree (EC20 = 4.9 µmol g-1) and

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Thompson (EC20 = 0.4 µmol g-1) sites, and the difference between EC20s expressed as (NiSEM-

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AVS)/ƒOC (RD = 1.70) was much greater than the difference between locations for NiSEM. The

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increased relative difference in the (NiSEM-AVS)/ƒOC model is partly due to the fact that sites

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with high concentrations of AVS relative to NiSEM, which should be indicative of no toxicity, had


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much lower Ephemeridae relative abundances (14% and 3%) than the baseline relative

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abundance (43%).

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Considering the critical role of Fe identified in the multivariate community analysis and

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the differences in NiHFO between sites, we related Ephemeridae relative abundance to an index of

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sediment Ni that accounts for binding by metal oxide minerals and that can be directly compared

343

to the AVS-based model. We found that the relative abundance of Ephemeridae declined with

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increasing concentrations of Ni not bound to amorphous Fe (Figure 3C). Considering the relative

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difference between concentration–response thresholds, the Fe-corrected Ni model did not

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decrease inter-site variation substantially more than the already low NiSEM model. However,

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while (NiSEM-NiHFO)/ƒOC is a coarse measure of Fe binding, the difference in EC20 thresholds

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between Thompson (EC20 = 6.3 µmol g-1) and Birchtree (EC20 = 4.8 µmol g-1) locations

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expressed as (NiSEM-NiHFO)/ƒOC was much smaller (RD = 0.27) when compared to (NiSEM-

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AVS)/ƒOC (RD = 1.70).

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Discussion

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In this study, we assessed if the spatially stratified geochemistry of field-contaminated

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sediments improved our ability to predict the effects of nickel exposure to benthic

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macroinvertebrates compared to current methods that partly overlook the distinct

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physicochemistry of oxic and suboxic sediments. Moreover, we used the natural contamination

357

gradient of nickel to determine field-based concentration–response thresholds using indigenous

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benthic macroinvertebrates. Though our field-based assessment involved organism exposure to

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nickel in both overlying water and sediment, the metal gradient at the study sites along with our



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multivariate analysis allowed us to distinguish whether macroinvertebrate taxa were responding

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to either sediment or overlying water impairment.

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As expected, we observed a substantial effect of mine effluent discharge on water quality,

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largely related to the input of ions in effluent-exposed tributaries. We observed a significantly

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higher dissolved nickel concentration in Thompson effluent-exposed tributaries, which was

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unexpected considering that the effluent-exposed tributary at the Thompson mine had slightly

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lower NiTOT than at the Birchtree mine. The difference in dissolved nickel in the tributaries can

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likely be attributed to differences in the effluent discharge at the two mines. First, the Birchtree

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mine effluent was inactive (i.e., summer shutdown) during the sampling period. Second, the

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Thompson mine effluent flows through a wetland complex (approximately 9 km) before reaching

370

the sampled tributary, whereas the Birchtree effluent discharges directly upstream of the sampled

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tributary. The long flow path between outflow and sampling location at the Thompson tributary

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likely results in more consistent dissolved Ni concentrations, while the Birchtree site would be

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more responsive to mine operating conditions. Finally, the Birchtree mine effluent treatment

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plant was updated in 2008 which substantially decreased metal loading due to 87% reduction in

375

dissolved nickel concentration and 40% increase in discharge capacity.34 Previous environmental

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monitoring of the Birchtree effluent-exposed tributary during regular mine activity reported

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dissolved nickel concentrations on average 2.5× higher than the 9 ± 8 µg L-1 observed in this

378

study.34 This difference in overlying water Ni concentration between mine sites likely explains

379

why dissolved Ni did not improve RDAWATER model fit, and also indicates that water–sediment

380

partitioning of Ni may be more stable in Thompson sediment over both long and short

381

timescales.


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As hypothesized, we observed distinct vertical variation in sediment chemistry and metal

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speciation in both Birchtree and Thompson sediment. AVS concentrations were substantially

384

lower in surface sediment when compared to deep sediment. This distinct difference is despite

385

the fact that surface samples (i.e. top 2 cm) were likely not discrete samples containing only

386

oxidized sediment. The oxic layer of fine-grained sediments is often 0.2).



Ephemeridae abundance (%)

50

A

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C

B

Birchtree Thompson

40 30 20 10 0 0.1

736

1.0

10.0

NiSEM (µmol g-1)

10

100

1000

1

10

100

-1 (NiSEM-AVS)/ƒOC (µmol g-1) (NiSEM-NiHFO)/ƒOC (µmol g )

737

Figure 2. Concentration–response relationships between Ephemeridae relative abundance and

738

select sediment nickel variables: bioavailable sediment nickel (A), bioavailable nickel as a

739

function of AVS corrected for organic carbon (B), bioavailable Ni not associated with

740

amorphous metal oxides corrected for organic carbon (C). Solid (Thompson mine, open

741

symbols) and dashed (Birchtree mine, closed symbols) lines are best-fit lines from least-squares

742

regression for effluent-exposed sites at each mine location. Crosses (+) on panel B indicate sites

743

with AVS concentration in excess of NiSEM (negative NiSEM-AVS) for which the minimum

744

positive value measured in the corresponding mine and tributary was assigned to meet

745

requirements of log-linear regression. The latter sites are also identified by crosses (+) on panels

746

A and C. Concentration–response relationships and relative differences in EC20s show that the

747

amorphous iron-corrected bioavailability model was more effective in normalizing the

748

concentration–response relationship between Birchtree and Thompson mines than the AVS-

749

based model. Note the log scale on the x-axis of each panel.


Metal Oxides in Surface Sediment Control Nickel Bioavailability to

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